In recent years, the income of individual dairy cow breeders has
declined due to the increase in feed prices and labor costs. This has
led to the rapid development of large-scale farming [1]. Although
large-scale centralized breeding has many advantages, it also brings
many new problems. In particular, excessive concentration of excreta
causes a large number of harmful dipteral insects, which attack and
cause stress to dairy cows. This not only causes changes in the normal
behavior of cows, but also causes decreases in diet and milk production
and imbalances in homeostasis and weakens immunity. The spread of
disease through bites puts a great constraint on the health and
productivity of dairy cows and affects the development of the dairy
industry [2-4]. Self-protective behavior describes the instinct of
animals to protect their own body and maintain stable physiological
indices. Studies show that when dipteral insects invade, dairy cows
exhibit defensive self-protective behaviors such as tail swishing, head
throwing, leg stamping, ear twitching, and skin twitching [5]. There is
a positive correlation between the self-protective behaviors of dairy
cows and the number of dipteral insects irritating dairy cows'
skin. Observing the behavior of dairy cows helps to understand the
activities and living patterns of cows under different living conditions
and to predict their future behaviors [6]. The research on the
self-protective behaviors of dairy cows is helpful to evaluate the
breeding environment and animal welfare status, which has great
practical significance.

The milk production of dairy cows differs greatly among species.
This is determined by the individual temperament types of dairy cows
[7]. The team of Ao Ri proposed that the degree to which dairy cows are
affected by insects is related to temperament type. Even if cows of
different temperament types are kept under the same environmental
conditions, the self-protective behaviors displayed when exposed to
insects have obvious differences [8]. It is believed that the study of
the differences in the self-protective behaviors of dairy cows is of
great significance for temperament breeding. At present, most of the
research in this area is based on artificial observation methods [2-8],
which usually requires at least two people. This manual observation
method has high work intensity and lower efficiency and accuracy, which
restricts the progress of the research.

Research on the behavior of dairy cows is mainly carried out by
monitoring physical parameters and physiological indicators. There are
two main methods for automatic monitoring. One method is to install
sensors on the cows, but interference from the sensors being shaken or
bumped will affect the accuracy of test data. The other method is to use
machine vision, but the existing literature does not report on automatic
detection of dairy cows' self-protective behaviors. For example,
Nadimi et al. used wireless sensors to measure the rotational angle and
speed of cows' necks [9]. Kwong monitored cows' disease and
lameness through wireless sensors [10]. Martiskainen used 3D sensors and
support vector machines to automatically detect cows' daily
behavior patterns [11]. Liu Dong et al. in 2016 proposed a hybrid
Gaussian model dynamic background modeling method that can effectively
track a single moving cow for 15 seconds, but this method cannot track a
stationary foreground object for a long time [12]. Gu et al. proposed to
analyze the movement behavior of cows in a tunnel by detecting the
characteristics of the cows' hooves and backs based on the minimum
bounding box and contour map [13]. Xiao et al. used ellipse fitting and
minimum distance matching methods that minimize the cost function to
track the target based on multiobjective segmentation, which can
effectively detect information on pigs' motion [14]. However, it is
mentioned that using the automatic single-threshold foreground
segmentation method is not good for the identification of colored pigs;
nor is it suitable for the black and white bodies of cows in this
article. By using the Kinect method based on the multisensor 3D image
acquisition device, Zhao et al. achieved accurate segmentation of body
regions such as heads, necks, torsos, and limbs of dairy cows in depth
images [15]. Deng et al. realized the identification of cows' body
parts based on depth images. However, this kind of depth image
acquisition device cannot detect the movement of tail swishing, so this
method is not suitable for the detection of cows' self-protective
behaviors [16].

At present, many works have shown being efficient for recognizing
actions in videos [17]. Artificial neural network is also a common tool
for motion recognition [18]. Most studies of cow tracking are based on
target segmentation; there are also studies on segmented identification
of dairy cows using depth images. It is easy to identify the cow's
head and trunk due to the obvious image characteristics. Many scholars
have completed the identification of cows' body parts but have been
unable to detect the tail features, because the regions of the tail and
legs are very similar. Detecting tail features is important because tail
swinging is one of the most frequent and characteristic movements of
self-protective behavior. Therefore, it can be seen that there is still
a great deal of research space and practical need for the identification
of cows' self-protective behaviors.

Tail swishing, head throwing, and leg stamping are the most typical
self-protective behaviors in dairy cows. These are very different from
normal studied cow behaviors, such as walking, standing, lameness,
lying, and resting. According to experts, the cow's self-protective
behavior generally lasts for about 1-2 seconds each time; therefore, the
algorithm requires higher real-time accuracy. Due to the complex scene
and the influence of occlusion and with the foreground targets being
black and white dairy cows, which have a low degree of differentiation
from the background, the difficulty of identifying self-protective
behavior is increased.

The adoption of computer vision technology could solve the problems
of relying on labor. In the process of high-intensity repetitive
production, machine vision technology can greatly liberate the labor
force and improve automation efficiency. Therefore, it is vital to study
a method based on video analysis and tracking to replace traditional
observational analysis.

2. Principles and Methods

Using the optical flow method to track every pixel in the image
makes the running speed very slow. At the same time, fewer corner points
will result in incomplete target information and reduces the accuracy of
detection. Combined with the morphological characteristics of head, leg,
and tail self-protective behaviors, the optical flow tracking algorithm
based on Shi-Tomasi corner detection method is improved by eliminating
the number of nontarget corner points and supplying the number of target
corner points. The calculation efficiency and the accuracy of moving
target detection are improved. The flow chart of the proposed method is
shown in Figure 1, and the specific steps are as follows:

(2) Delete the corner points that do not move continuously by using
the movement information of the frames.

(3) If the distance of the tracking point trajectory is greater
than the threshold, the method is to retain the feature point and
exclude the nonforeground target feature point.

(4) When the number of corner points is too small, corner detection
is performed again to provide a sufficient number of feature points for
optical flow detection.

(5) Use a bounding box of corner points to track self-protective
behavior, which could effectively distinguish the different behavior
characteristics and provide the necessary feature vector parameters.

The Shi-Tomasi corner detection algorithm is based on an
improvement to the Harris corner detection algorithm [19]. Compared with
the Harris corner detection algorithm, a nonmaximum suppression step is
added, and adjacent Harris corners are removed. The problem of feature
points clusters is solved, so that the distribution of corner points
becomes more uniform in the detection of the head, legs, and tail area,
while reducing the number of feature points.

The Lucas-Kanade optical flow algorithm [20] is a method of
detecting feature points by performing two frames; it is used for sparse
optical flow tracking with a relatively small number of feature points.
The pyramid Lucas-Kanade optical flow algorithm is based on the improved
Lucas-Kanade optical flow method. It has a wider range of applications
and has a good detection effect for fast motion or discontinuous motion.
Pyramid optical flow tracking starts at a larger spatial scale and is
then gradually corrected along the pyramid to determine the motion
speed.

Through the improved tracking method proposed in this paper, the
computational complexity of the tracking algorithm can be greatly
reduced. Although the number of feature points is reduced, the
distribution of feature points is optimized, and the tracking of feature
points can accurately represent the pixels of the self-protective
behavior.

The experimental site is at Xuri Ranch, Tuoketuo County, Hohhot,
Inner Mongolia. An independent cowshed of 2.5 m * 2.5 m is built. The
outside of the semiclosed cowshed is surrounded by mesh yarn. A
Panasonic HDC-HS100 HD digital video camera is used to track 10 healthy
Holstein cows; only one Holstein cow is photographed at a time.
Considering that self-protective behavior involves detailed action of
the body, the distance is within 3 m, and the height is about 1.4 m for
video tracking, at a rate of 25 frames per second.

3.2. Test of Feature Point Detection Algorithm. This article mainly
analyzes the typical characteristics of dairy cows' self-protective
behavior. This behavior is characterized by sudden and instantaneous
movement. In order to improve the efficiency of system implementation,
the number of feature points can be optimized. When the target is
tracked for the first time, the corner detection method obtains more
corner points to initialize the detection image in Figure 2(a), and many
corner points are not the foreground target area. When using the optical
flow method to track all the feature points, most of the nonmoving
feature points waste a lot of computing resources. Figure 2(b) shows the
trajectory map that is tracked by the optical flow method. It can be
seen that many moving feature points are not tracked for a while, and
the effective feature points are not supplied in time, resulting in
failure to track behavior. By using the movement information of the
frames, deleting corners that do not continue to move can effectively
reduce the number of corner points. When the number of effective corner
points is too small, corner detection continues again in order to
provide a sufficient number of feature points for optical flow
detection; this reduces the complexity of the algorithm and improves the
detection efficiency. The small circles in Figure 2(c) are the detected
feature points. Most of the corners describe self-protective behavior
characteristics. At the same time, some nonforeground targets, such as
blown gauze in the background, are also detected. In order to avoid
these nonforeground targets interfering in the detection of
self-protective behavior, the feature points are retained if the
tracking distance is greater than the given threshold. This method can
effectively reduce the number of nonforeground target feature points
such as gauze. Figure 2(d) is the result of tracking self-protective
behavior using a bounding box. The bounding box can effectively describe
the behavior characteristics and provide the necessary feature vector
parameters.

3.3. Detection of Self-Protective Behavior in Complex Conditions.
In the feeding bar, parts of the cows' bodies are often blocked by
the railings. The algorithm proposed in this paper can realize the
detection of the blocked body in a complex environment. The corner
points can still be attached to the unblocked body area. The nonmoving
feature points are eliminated first, and the continuously moving feature
points can still be effectively tracked using the optical flow method.
Some feature points will lose tracking information because they have
moved to the blocked area, but constantly supplementing the new feature
point number ensures the continuous tracking of the moving
self-protective behavior. By extracting the feature vector of the
bounding box, the common trajectory of multiple moving feature points
can be seen as a whole, thereby effectively detecting the blocked
self-protective behavior.

In Figure 3(a), the tracking effect is better when the
self-protective behavior features are not blocked. Even if the cow is
facing away from the camera and the tail is blocked by the fence, the
bounding box can still describe the characteristics of the movement, and
the self-protective behavior movement can be effectively detected in
time.

In Figure 3(b), the guardrails block some areas of the cow's
head and body, but this does not affect the effectiveness of the
algorithm's detection. It can continue tracking the cow's head
and tail movement feature points, track the movement trajectory, and
accurately and effectively identify self-protective behavior.

3.4. Artificial Neural Network. 20 typical unblocked videos were
selected, including 2840 training samples of tail swishing, 2310
training samples of leg stamping, and 1160 training samples of head
throwing. This article extracts 9-dimensional feature vectors: rotation
angle, moving distance, horizontal and vertical displacement of feature
points, starting point and end point ordinates, area of bounding box,
height to width ratio, and height difference in a vertical direction. In
order to realize the artificial intelligence to identify cows'
self-protective behaviors, an artificial neural network model was
established using MATLAB R2017a artificial neural network toolbox on the
basis of extracting the characteristic values of dairy cows'
self-protective behaviors. The input layer of the artificial neural
network is set to 9 (9-dimensional feature vectors), and the output
layer is set to 3 (tail swishing, head throwing, and leg stamping).

By using this artificial neural network model, the proportion of
training samples, verification samples, and test data needs to be set.
This article sets the training sample to 80%, the validation sample to
10%, and the test sample to 10%. The smaller the cross-entropy, the
better the classification effect. To prevent overfitting, a validation
set is used to improve the accuracy of the classifier model. If the
cross-entropy value cannot be reduced for 6 consecutive iterations, the
iteration is stopped. As shown in Figure 4(a), the model stops after 19
iterations, while the cross-entropy minimum value is 0.010578 and the
best validation performance is at epoch 13. The model shown in Figure
4(b) is a convergence that stops after 54 iterations. The cross-entropy
value is 0.0001927. It can be seen that the accuracy of this model is
higher.

The receiver operating characteristic (ROC) curve can be used to
determine the merits of the classifier model. The true positive rate
(TPR) of the classification model is the ordinate and the false positive
rate (FPR) is the abscissa. The ROC curve is plotted in Figure 5. The
artificial neural network model has a high accuracy and can be used as
an effective detection and identification model for dairy cows.

4. Discussion

In order to accurately evaluate the accuracy and effectiveness of
the algorithm, this paper compares its accuracy and real-time
performance.

4.1. Contrast with Artificial Experience. To evaluate the accuracy
of the detection algorithm, a video of a typical cow's
self-protective behaviors was selected for the collected 100-hour video.
20 sets of video samples were selected, each of which included tail
swishing, head throwing, and leg stamping behavioral actions. Because
other video detection methods cannot effectively identify
self-protective behaviors, this article only compares with artificial
statistics. When a behavior is artificially marked as one of the three
types of self-protective behaviors if the system also recognizes the
behavior as a corresponding body behavior it is denoted as TP (True
Positive), otherwise as FN (False Negative). When a behavior is not
artificially marked as one of the three self-protective behaviors, if
the system could not recognize the corresponding self-protective
behavior, it is denoted as FP (False Positive); otherwise it is denoted
as TN (True Negative). The accuracy rate reflects the proportion of true
test results that are really true to all test results, and the recall
rate reflects the proportion of results that are correctly detected as
true and the results of manual test results are all true. The formula is
as follows: Accuracy (precision) = TP (correct detection) / (TP + FP);
recall = TP (correct detection) / (TP + FN).

Figure 6 shows the number of tail swishing, head throwing, and leg
stamping incidents using the algorithms proposed in this paper and
artificial statistics. As can be seen from Figure 6, the accuracy of the
detection algorithm in this paper is in the range of [0.88, 1] for tail
swishing, head throwing, and leg stamping, and [0.87,1] for recall,
indicating that the detection algorithm is close to the artificial
statistics.

4.2. Comparison of Detection Effect. Figure 7(a) is an effect
diagram of an adaptive threshold frame difference method. The frame
difference method can detect moving feature points while a cow is
walking, but it cannot effectively distinguish between the
characteristic pixel points of a self-protective behavior and a normal
motion behavior. Figure 7(b) shows the result using a Gaussian mixture
model. Although the pixel feature points of the self-protective behavior
were detected using the Gaussian mixture model, erroneous detection due
to bodily shaking and jittering could not be avoided. The feature points
of the behavior cause a lot of interference, which makes it impossible
to effectively track and identify the self-protective behavior.

Figure 7(c) is the result of the improved Shi-Tomasi corner
detection optical flow tracking algorithm based on the motion
characteristics of the swinging tail proposed in this paper. The box is
the tail bounding box; the dot is the tracking feature point, and the
line is the tail trace feature. The trajectory of a point can be clearly
tracked by using this description method; the position and the
trajectory of the tail can be clearly observed, and the pixels having
the characteristics of body protection can be effectively traced, which
can reduce the number of corner points and the computational complexity
of the algorithm to improve the detection accuracy. It is better than
the adaptive threshold frame difference method and the Gaussian mixture
background model, which could find the characteristics of
self-protective behaviors and realize intelligent identification and
classification.

4.3. Comparison of Time Complexity. To compare and analyze the
running speeds of different detection algorithms, we randomly select 10
groups of videos from the collected 100-hour video. The average running
time of each of the 6 different segmentation algorithms is shown in
Figure 8.

The average running times of the different algorithms are as
follows: direct optical flow method 1835ms; Harris corner optical flow
method 1104ms; adaptive frame difference method 214ms; Gaussian mixture
model 616ms; Shi-Tomasi corner optical flow algorithm 984ms; and the
proposed method 251ms. The results show that the proposed algorithm is
stable and effective. The method proposed in this paper saves time and
greatly improves efficiency.

5. Conclusion

This paper proposes an improved optical tracking algorithm based on
Shi-Tomasi corner detection for intelligent classification and
identification of three typical self-protective behaviors in cows. The
main conclusions are as follows:

(1) The targets detected in this paper are three typical
self-protective behaviors in dairy cows: tail swishing, head throwing,
and leg stamping. Feature vectors are extracted based on the
characteristics of the movements to find out the outline of the target.
Using the artificial neural network to establish a classifier model can
effectively classify cows' tail swishing, head throwing, and leg
stamping.

(2) By combining the morphological characteristics of head, leg,
and tail movements, the interference of background movement is
eliminated. At the same time, the number of effective corner points is
added in time to ensure the accuracy of detection. The test results show
that, compared with the manual statistics, the accuracy rate range of
this method is [0.88,1] and the recall rate range is [0.87,1].

(3) Using the method of decreasing nonmoving feature points and
setting the tracking trajectory displacement threshold proposed in this
paper, the calculation of corner detection is greatly reduced. The
experimental results show that the computation time of this method is
lower than that of the Shi-Tomasi corner point optical flow algorithm,
which could save time and greatly improve efficiency. Blocked
self-protective behaviors can be detected more accurately, which shows
that the proposed detection algorithm has strong robustness.

The method can automatically and accurately track and detect
cows' self-protective behaviors, provide effective and accurate
statistics for animal behavior researchers, and help them for further
research in this field.

https://doi.org/10.1155/2018/9106836

Data Availability

The data used to support the findings of this study are available
from the corresponding author upon request.